This project involves the development of new statistical methodologies and computational tools for network-based integrative analysis of epigenetic risk factors of cardiovascular diseases (CVD). While the advent of omics data from new technologies has facilitated the study of epigenetic factors, existing methodologies often do not account for complexities of biological data such as correlations due to interactions of genes/proteins as part of biological pathways and fail to e?ciently integrate diverse omics data sets for instance genetic variation, DNA methylation and gene expression. The methodologies proposed in this project, and the software tools that will be developed to implement them, address these shortcomings, and facilitate further research by the biomedical community to gain a better understanding of the underlying biology of CVD, and to develop new diagnostic biomarkers and potential targets for therapies. The proposed methodologies are motivated by the study of epigenetic data from the Multi-Ethnic Study of Atherosclerosis (MESA), and include (i) a network-based pathway enrichment analysis method that incorporates available knowledge of interactions among genes and proteins while complementing and re?ning such information (Aim 1A), as well as its extension for analysis of multiple types of omics data (Aim 1B), and (ii) an integrative analysis framework to identify associations among gene expression levels and DNA methylation (Aim 2A) and identify common epigenetic factors of multiple CVD phenotypes through integrated analysis of DNA methylation and mRNA expression data (Aim 2B). We will develop e?cient and user-friendly software tools for the proposed methods (Aim 3), which will be made freely available to the public after extensive tests using both simulated data, as well as real data from MESA.
The proposed research addresses the need for development of new statistical machine learning methods for analysis of diverse epigenetic data from multiple cardiovascular disease outcomes. The proposed training activities are designed to enhance the applicant's knowledge of epigenetics and physiology of cardiovascular diseases (CVD), and to further his career as an independent investigator in the area of computational epigenetics for CVD.
|Mathur, Ravi; Rotroff, Daniel; Ma, Jun et al. (2018) Gene set analysis methods: a systematic comparison. BioData Min 11:8|
|Randolph, Timothy W; Zhao, Sen; Copeland, Wade et al. (2018) KERNEL-PENALIZED REGRESSION FOR ANALYSIS OF MICROBIOME DATA. Ann Appl Stat 12:540-566|
|Wang, Xiaoliang; Shojaie, Ali; Zhang, Yuzheng et al. (2017) Exploratory plasma proteomic analysis in a randomized crossover trial of aspirin among healthy men and women. PLoS One 12:e0178444|
|Chen, Shizhe; Witten, Daniela; Shojaie, Ali (2017) Nearly assumptionless screening for the mutually-exciting multivariate Hawkes process. Electron J Stat 11:1207-1234|
|Miles, Fayth L; Navarro, Sandi L; Schwarz, Yvonne et al. (2017) Plasma metabolite abundances are associated with urinary enterolactone excretion in healthy participants on controlled diets. Food Funct 8:3209-3218|
|Chen, Shizhe; Shojaie, Ali; Witten, Daniela M (2017) Network Reconstruction From High-Dimensional Ordinary Differential Equations. J Am Stat Assoc 112:1697-1707|
|Seshadri, Chetan; Sedaghat, Nafiseh; Campo, Monica et al. (2017) Transcriptional networks are associated with resistance to Mycobacterium tuberculosis infection. PLoS One 12:e0175844|
|Sedaghat, Nafiseh; Fathy, Mahmood; Modarressi, Mohammad Hossein et al. (2016) Identifying functional cancer-specific miRNA-mRNA interactions in testicular germ cell tumor. J Theor Biol 404:82-96|
|Ma, Jing; Shojaie, Ali; Michailidis, George (2016) Network-based pathway enrichment analysis with incomplete network information. Bioinformatics 32:3165-3174|
|Sas, Kelli M; Kayampilly, Pradeep; Byun, Jaeman et al. (2016) Tissue-specific metabolic reprogramming drives nutrient flux in diabetic complications. JCI Insight 1:e86976|
Showing the most recent 10 out of 14 publications